Hardware device based software verification

ABSTRACT

A method and system for improving an operation of an automated IT system is provided. The method includes identifying pre-tested software applications associated with requirements of processes executed by a hardware device with respect to an IT system. A list of available software applications associated with required features is generated and each feature is defined such that the currently available software applications are configured to provide and execute the required features. Evaluation code is executed and a resulting a list of validated software applications is generated. A list of short listed software applications and identification software code enabling an automated encoder learning process are generated. A software operational solution is identified and modification code is generated and executed code resulting in improved operation of the validated software applications and the hardware device.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application claiming priority to Ser.No. 15/472,991 filed Mar. 29, 2017, the contents of which are herebyincorporated by reference.

FIELD

The present invention relates generally to a method for validatingsoftware applications and in particular to a method and associatedsystem for improving software technology associated with an operation ofa hardware device via generation and execution of software code formodifying software code and associated hardware devices.

BACKGROUND

Accurately validating instructions for operating a device typicallyincludes an inaccurate process with little flexibility. Determiningsystem operational solutions with respect to code may include acomplicated process that may be time consuming and require a largeamount of resources. Accordingly, there exists a need in the art toovercome at least some of the deficiencies and limitations describedherein above.

SUMMARY

A first aspect of the invention provides an automated IT systemoperational improvement method comprising: identifying, by a processorof a hardware device of an IT system, a set of pre-tested softwareapplications associated with requirements of processes executed by thehardware device with respect to the IT system, wherein the requirementsare comprised by metadata, and wherein the set of pre-tested softwareapplications have been pre-tested for accurate functionality withrespect to performing the processes executed by the hardware device withrespect to the IT system; generating, by the processor based on resultsof the identifying, a list of currently available software applicationsof the set of pre-tested software applications associated with requiredfeatures associated with software based solutions for modifying the setof pre-tested software applications such that the currently availablesoftware applications are configured to provide and execute the requiredfeatures; defining, by the processor based on results of the generating,each required feature of the required features; generating, by theprocessor based on the software based solutions, evaluation code forevaluating the currently available software applications with respect toassociations between each the required feature and a plurality ofhardware based data repositories; generating, by the processor based onresults of executing the evaluation code, a list of validated softwareapplications of the currently available software applications, thevalidated software applications associated with each the requiredfeature being executed by applying Euclidean distance software code;generating, by the processor based on results of the executing theevaluation code, a list of short listed software applications of thecurrently available software applications, the short listed softwareapplications have been verified via a sampling software test determinedby feature translation learning code associated with each the requiredfeature being executed; generating, by the processor, identificationsoftware code enabling an automated encoder learning process applying afeature learning technique for identifying the validated softwareapplications; identifying, by the processor, a software operationalsolution of the software based solutions, the software operationalsolution comprising all available features of the required featuresassociated with the validated software applications and comprising agreater number of features than any other software application of thevalidated software applications; generating, by the processor based onthe software operational solution, modification code for modifying thevalidated software applications; and executing, by the processor, themodification code resulting in improved operation of the validatedsoftware applications and the hardware device.

A second aspect of the invention provides a computer program product,comprising a computer readable hardware storage device storing acomputer readable program code, the computer readable program codecomprising an algorithm that when executed by a processor of a hardwaredevice of an IT system implements an automated IT system operationalimprovement method, the method comprising: identifying, by theprocessor, a set of pre-tested software applications associated withrequirements of processes executed by the hardware device with respectto the IT system, wherein the requirements are comprised by metadata,and wherein the set of pre-tested software applications have beenpre-tested for accurate functionality with respect to performing theprocesses executed by the hardware device with respect to the IT system;generating, by the processor based on results of the identifying, a listof currently available software applications of the set of pre-testedsoftware applications associated with required features associated withsoftware based solutions for modifying the set of pre-tested softwareapplications such that the currently available software applications areconfigured to provide and execute the required features; defining, bythe processor based on results of the generating, each required featureof the required features; generating, by the processor based on thesoftware based solutions, evaluation code for evaluating the currentlyavailable software applications with respect to associations betweeneach the required feature and a plurality of hardware based datarepositories; generating, by the processor based on results of executingthe evaluation code, a list of validated software applications of thecurrently available software applications, the validated softwareapplications associated with each the required feature being executed byapplying Euclidean distance software code; generating, by the processorbased on results of the executing the evaluation code, a list of shortlisted software applications of the currently available softwareapplications, the short listed software applications have been verifiedvia a sampling software test determined by feature translation learningcode associated with each the required feature being executed;generating, by the processor, identification software code enabling anautomated encoder learning process applying a feature learning techniquefor identifying the validated software applications; identifying, by theprocessor, a software operational solution of the software basedsolutions, the software operational solution comprising all availablefeatures of the required features associated with the validated softwareapplications and comprising a greater number of features than any othersoftware application of the validated software applications; generating,by the processor based on the software operational solution,modification code for modifying the validated software applications; andexecuting, by the processor, the modification code resulting in improvedoperation of the validated software applications and the hardwaredevice.

A third aspect of the invention provides a hardware device of an ITsystem comprising a processor coupled to a computer-readable memoryunit, the memory unit comprising instructions that when executed by thecomputer processor implements an automated IT system operationalimprovement method comprising: identifying, by the processor, a set ofpre-tested software applications associated with requirements ofprocesses executed by the hardware device with respect to the IT system,wherein the requirements are comprised by metadata, and wherein the setof pre-tested software applications have been pre-tested for accuratefunctionality with respect to performing the processes executed by thehardware device with respect to the IT system; generating, by theprocessor based on results of the identifying, a list of currentlyavailable software applications of the set of pre-tested softwareapplications associated with required features associated with softwarebased solutions for modifying the set of pre-tested softwareapplications such that the currently available software applications areconfigured to provide and execute the required features; defining, bythe processor based on results of the generating, each required featureof the required features; generating, by the processor based on thesoftware based solutions, evaluation code for evaluating the currentlyavailable software applications with respect to associations betweeneach the required feature and a plurality of hardware based datarepositories; generating, by the processor based on results of executingthe evaluation code, a list of validated software applications of thecurrently available software applications, the validated softwareapplications associated with each the required feature being executed byapplying Euclidean distance software code; generating, by the processorbased on results of the executing the evaluation code, a list of shortlisted software applications of the currently available softwareapplications, the short listed software applications have been verifiedvia a sampling software test determined by feature translation learningcode associated with each the required feature being executed;generating, by the processor, identification software code enabling anautomated encoder learning process applying a feature learning techniquefor identifying the validated software applications; identifying, by theprocessor, a software operational solution of the software basedsolutions, the software operational solution comprising all availablefeatures of the required features associated with the validated softwareapplications and comprising a greater number of features than any othersoftware application of the validated software applications; generating,by the processor based on the software operational solution,modification code for modifying the validated software applications; andexecuting, by the processor, the modification code resulting in improvedoperation of the validated software applications and the hardwaredevice.

The present invention advantageously provides a simple method andassociated system capable of accurately validating instructions foroperating a device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for improving hardware device operation bygenerating evaluation and modification software code for validatingsoftware applications and a hardware device, in accordance withembodiments of the present invention.

FIG. 2 illustrates an algorithm detailing a process flow enabled bysystem 100 of FIG. 1 for improving hardware device operation bygenerating evaluation and modification software code for validatingsoftware applications and a hardware device, in accordance withembodiments of the present invention.

FIG. 3 illustrates execution of a step of FIG. 2 for generating a listof currently available software applications of the set of pre-testedsoftware applications is generated, in accordance with embodiments ofthe present invention

FIG. 4 illustrates execution of a step of FIG. 2 for evaluatingcurrently available software applications with respect to associationsbetween each required feature and a plurality of hardware based datarepositories, in accordance with embodiments of the present invention.

FIG. 5 illustrates execution of a step of FIG. 2 for generating a listof validated software applications associated with each requiredfeature, in accordance with embodiments of the present invention.

FIG. 6 illustrates execution of a step of FIG. 2 for generatingidentification software code 600, in accordance with embodiments of thepresent invention

FIG. 7 illustrates execution of a step of FIG. 2 for generating softwareoperational solution 700, in accordance with embodiments of the presentinvention.

FIG. 8 illustrates a computer system used by the system of FIG. 1 forenabling a process for improving hardware device operation by generatingevaluation and modification software code for validating softwareapplications and a hardware device, in accordance with embodiments ofthe present invention.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 for improving hardware device operationby generating evaluation and modification software code for validatingsoftware applications and a hardware device 14, in accordance withembodiments of the present invention. System 100 enables a process forimproving hardware/software technology by enabling an automated encoderfor applying a feature learning technique for identifying and modifyingvalidated software applications. System 100 is enabled to automaticallyselect software for an operational improvement project by mappingsoftware/hardware requirements to IT system frameworks determiningassociated capabilities and processes. System 100 enables a process forselecting a software application executing code for implementing bestdepth capabilities of a software project by identifying requirements anduse cases of a software project that are associated with selectedsoftware offerings. System 100 executes code for calculating a depth fitscore (for each selected software application) for evaluating a depth ofthe selected software application by classifying operational featuresvia usage of an unsupervised feature translation learning andcharacteristic classification hardware framework. Additionally, system100 enables a depth criteria adjustment process for selecting softwarefor absorbing modifications in a depth fit for modifying and utilizingsyndication software with respect to a trained data set configured toaccurately evaluate a depth fit of features for each softwareapplication.

System 100 of FIG. 1 includes a server 23 and a database system 21connected through a network 7 to an IT system comprising a hardwaredevice 14. IT system 29 may comprise a plurality of hardware devices(similar to hardware device 14) and/or any type of IT type of device.Hardware device 14 comprises testing/evaluation circuitry/logic 12,validation/sampling circuitry/logic 15, and a memory system. Memorysystem comprises software applications 17 and software code 28. Server23, database system 21, and hardware device 14 each may comprise anembedded device. An embedded device is defined herein as a dedicateddevice or computer comprising a combination of computer hardware andsoftware (fixed in capability or programmable) specifically designed forexecuting a specialized function. Programmable embedded computers ordevices may comprise specialized programming interfaces. In oneembodiment, server 23, database system 21, and hardware device 14 mayeach comprise a specialized hardware device comprising specialized(non-generic) hardware and circuitry (i.e., specialized discretenon-generic analog, digital, and logic based circuitry) for(independently or in combination) executing a process described withrespect to FIGS. 1-3. The specialized discrete non-generic analog,digital, and logic based circuitry (e.g., testing/evaluationcircuitry/logic 12, validating/sampling circuitry/logic 15, etc.) mayinclude proprietary specially designed components (e.g., a specializedintegrated circuit, such as for example an Application SpecificIntegrated Circuit (ASIC) designed for only implementing an automatedprocess improving hardware device operation by generating evaluation andmodification software code for validating software applications and ahardware device 14. Hardware device 14 includes memory system 8comprising software applications 17 and software code 28. The memorysystem 8 may include a single memory system. Alternatively, the memorysystem 8 may include a plurality of memory systems. Network 7 mayinclude any type of network including, inter alia, a local area network,(LAN), a wide area network (WAN), the Internet, a wireless network, etc.

System 100 enables a process for determining pretested softwareapplications (i.e., pretested for accurate functionality with respect toperforming processes executed by hardware device 14 with respect to ITsystem 29) and associated software platforms required to fulfill a givenset of hardware/software based operational requirements (i.e.,functional and non-functional requirements). System 100 comprises anautomated IT system operational improvement method for identifyingavailable software applications associated with operational requirementsof processes executed by hardware device 14 with respect to IT system29. System 100 provides a hardware framework for cognitively identifyingsoftware based solutions that include all available features associatedwith validated software applications and executing modification coderesulting in improved operation of the validated software applicationsand hardware device 14.

System 100 enables:

A process for combining software code into software/hardware operationalsolution requirements for automatically enabling hardware architectureto recursively generate modified framework based model generation code.The modified framework based model generation code executes decisionbased circuitry for generating a decision model and associatedhardware/software for ensuring that all identified operationalrequirements and are associated with specified software applicationsthereby generating robust hardware and software systems. The decisionmodel and associated hardware/software provides a mechanism forcalculating a fit score for each software application for executingdepth fit assortment circuitry thereby enabling a feature translationlearning circuit and associated characteristic classification frameworkfor classifying operational features for a depth based operationalevaluation. Additionally, the decision model and associatedhardware/software enables a process for fine tuning all operationalattributes of the IT system. A meta model may be generated forevaluating software/hardware based parameters including, inter alia, asoftware/hardware provider, references, a specified geography,non-functional requirements, etc. Meta data retrieved from the metamodels enables a process for extracting of the meta data from variousassociated databases. System 100 executes an accurate hardware/softwaremechanism for fine tuning weights for various target hardware/softwareselection framework parameters for each client by employing asyndication guided software sampling strategy and unsupervised featurelearning processes with respect to identified target software solutionoptions. The meta model allows for combining sentiment inputs forapplicable software based parameters from networking Websites byproviding the ability to include broader software attributes for scoringindividual parameters for a software/hardware offering selectionprocess.

FIG. 2 illustrates an algorithm detailing a process flow enabled bysystem 100 of FIG. 1 for improving hardware device operation bygenerating evaluation and modification software code for validatingsoftware applications and a hardware device, in accordance withembodiments of the present invention. Each of the steps in the algorithmof FIG. 2 may be enabled and executed in any order by a computerprocessor(s) executing computer code. Additionally, each of the steps inthe algorithm of FIG. 2 may be enabled and executed in combination byserver 23, database system 21, and/or hardware device 14 of FIG. 1. Instep 200, a set of pre-tested software applications associated withrequirements of processes executed by a hardware device with respect toan IT system are identified. The requirements are comprised by metadata.The set of pre-tested software applications have been pre-tested foraccurate functionality with respect to performing the processes executedby the hardware device with respect to the IT system. In step 202, alist of currently available software applications of the set ofpre-tested software applications is generated. The list includesrequired features associated with software based solutions for modifyingthe set of pre-tested software applications such that the currentlyavailable software applications are configured to provide and executethe required features. In step 204, each required feature is definedbased on results of step 202. In step 208, evaluation code forevaluating the currently available software applications with respect toassociations between each required feature and a plurality of hardwarebased data repositories is generated based on the software basedsolutions. In step 210, a list of validated software applicationsassociated with each required feature being executed by applyingEuclidean distance software code is generated. In step 212, a list ofshort listed software applications (of the currently available softwareapplications is generated based on results execution of the evaluationcode. The short listed software applications have been verified via asampling software test determined by feature translation learning codeassociated with each d required feature being executed. In step 214,identification software code is generated. The identification softwarecode enables an automated encoder learning process that applies afeature learning technique for identifying the validated softwareapplications. In step 218, a software operational solution (of thesoftware based solutions) is generated. The software operationalsolution includes: all available features of the required featuresassociated with the validated software applications and a greater numberof features than any other software application of the validatedsoftware applications. In step 220, modification code for modifying thevalidated software applications is generated (based on the softwareoperational solution) and executed resulting in improved operation ofthe validated software applications and hardware device. Improvedoperation of the validated software applications and hardware device mayinclude, inter alia, an improved processing speed for a processor, animproved memory structure of the hardware device, etc. An improvedmemory structure enables an improved access speed for accessing datawithin the improved memory structure via an internal layered structureof the improved memory structure. In step 224, executable codeassociated with the software operational solution is generated andexecuted resulting in modification of the validated softwareapplications with respect to improving an efficiency and accuracy of thevalidated software applications. Additionally, additional executablecode is generated in response to executing the executable code. Theadditional executable is combined with the validated softwareapplications resulting in improving efficiency and accuracy of thevalidated software applications. In step 228, sampling software code isgenerated by applying a sampling technique for enabling the hardwaredevice to execute self learning software code with respect to aplurality of database systems based on software based solutions. In step230, the validated software applications are associated to differingdimensions of specialized memory devices and/or structures.

FIG. 3 illustrates execution of step 202 of FIG. 2 for generating a listof currently available software applications of the set of pre-testedsoftware applications, in accordance with embodiments of the presentinvention. Execution of step 202 results in the generation of a mappingtable 300 for mapping software features 302 to software applications304. The list of currently available software applications such thateach software feature is defined and commonly used software applicationsare identified. The mapping table is generated by selecting mappingfunctions as follows:

An entire set of functional area hardware/software components isrepresented by a set {F}. Additionally, since set {F} represents the setof all functional components, set {F} may include a subset or a properset of a component functional schematic as follows: If, C comprises anumber of hardware/software component functions in the functionalschematic, then the following equation is generated:

{F}←{F _(c) |∀F _(i) ∈{CBM}::i<C}

Additionally, set {P} represents a set of all software products suchthat P_(α) comprises a subset of {P) defined as follows:

{P _(β) }←{P _(βi) |∀P _(i) ∈{P}::β=|P _(j) ::┌j┐=|F|;└j┘=1|}

Therefore, an ordered set that has been fitted for width may be executedsuch that a software selection process may be implemented with respectto the following assortment:

{(P _(β))}←{P _(sorted) ∩F _(c) ::∃P≠{Ø},(P _(i) >P _(i-1) |∀P _(i) ∈P)}

Additionally, each dimension of P may be defined as a functional ornon-functional dimension being captured in a set {C}←{set of allcategories}, such that:

{C}←{C _(c) |∀C _(i)∈{dimensions}::i<C}

The aforementioned mapping table generation process allows for theselection of functional hardware/software components and correspondingsoftware products across categories of functional and non-functionalrequirements.

FIG. 4 illustrates execution of step 208 of FIG. 2 for evaluatingcurrently available software applications with respect to associationsbetween each required feature and a plurality of hardware based datarepositories, in accordance with embodiments of the present invention.Execution of step 208 results in the identification of salient featuresvia usage of context aware syndication software by generating evaluationcode for evaluating all available software applications SA1 . . . SA12with respect to associations with respect to required functionalsoftware features F1 . . . F8 and associated data repositories. Eachdetermined association is recorded. The identification of salientfeatures via usage of context aware syndication software is determinedas follows:

The following Euclidian distance equations represent:

d _(proximity)(c ^(i) ,c ^(j))≡Eucliedian distance between c ^(i) ,c^(j)

d _(affinity)(c ^(i) ,c ^(j))≡Eucliedian distance between c ^(i) ,c ^(j)

Therefore, normalized the values for d_(proximity) and d_(affinity) to[0,1] are determined and a dissimilarity between the pair of data valuesare as follows:

${d\left( {x^{i},x^{j}} \right)} = \frac{d_{proximity}\left( {c^{i},c^{j}} \right)}{1 + {\theta \cdot {d_{affinity}\left( {c^{i},c^{j}} \right)}}}$

θ represents a constant and a maximum dimensionality allowed for theabove coloring problem is set as 4, therefore θ is set at 3 for enablinga higher degree of freedom required to resolve the problem ofdimensionality and therefore, θ=3. Based on the above calculations, itmay be determined that a data value is salient if d (c^(i),c^(j)) isvery high. Therefore, for every feature a query is performed to searchfor the K most similar data values (i.e., if the most similar datavalues are different from c^(i), then all data values are highlydifferent to c^(i)). Therefore, a syndication guided value of c^(i) isdefined as follows:

$S^{i} = {1 - {\exp \left\{ {{- \frac{1}{K}}{\sum\limits_{k = 1}^{K}{d\left( {c^{i},c^{j}} \right)}}} \right\}}}$

The above process identifies specialized software products thatdifferentiate with respect to a specified set of requirements in scopefor all evaluation parameters.

FIG. 5 illustrates execution of step 210 of FIG. 2 for generating a listof validated software applications associated with each requiredfeature, in accordance with embodiments of the present invention.Execution of step 208 results in generating feature translation learningsoftware code. Additionally, a list of validated software applications(i.e., which have delivered selected features and associated dimensionssuccessfully and consistently) is generated such that a samplingstrategy 500 is applied to identify software and data comprising adefective or a good sample. A shortlist sample application and data maybe enabled to apply learning code from the sample application to acomplete data set thereby modifying a strength of a software applicationfitment to a feature based on the learning code.

An unsupervised feature learning algorithm may be enabled to discoversoftware related features in unlabeled data. Additionally, the featuresmay be learned from a representative set of data values sampled from thedataset and the features may be applied across the population.Therefore, buckets of X are retrieved and a function is defined asfollows:

f:R ^(N) →R ^(K)

The above function maps a new vector c^(i) to a new feature vector by Kfeatures and an auto encoder is executed as a symmetrical neural networkthat is used to learn software features of the dataset in anunsupervised manner. The auto encoder is executed by minimizing areconstruction error between input data at the encoding layer and itsreconstruction at the decoding layer thereby enabling the followingequation:

α^(i) =f(x)=g(W ₁ c ^(i) +b ₁)

W₁∈R^(K×N) comprises a weight matrix with K features, b₁∈R^(K) comprisesan encoding bias, and g (x)=1/(1+e^(−X)) comprises a logistic sigmoidfunction.

Additionally, a vector may be decoded using a separate linear decodingmatrix as follows:

Z ^(i)=(W ₂ ^(T) α+b ₂)

W₂∈R^(K×N) comprises a weight matrix with K features and b₂∈R^(K)comprises an encoding bias.

Therefore, categories of software requirements are identified withrespect to a logical group of code to be sourced from one targetsoftware product.

FIG. 6 illustrates execution of step 214 of FIG. 2 for generatingidentification software code 600, in accordance with embodiments of thepresent invention. Execution of step 214 results in classifying ahardware framework for mapping features 601 a and 601 b to softwareapplications 604 by generating intelligence based code for identifyinghighly successfully executed software applications for each feature byapplying an auto encoder learning technique to remove any error in theselection process, such that:

1. Each required feature is associated to shortlisted softwareapplications via different dimensions.2. A software application's reverse association to a feature isdiscovered.

Feature extractor code within a data set is generated by minimizing acost function. A first term in a reconstruction equation comprises anerror term and a second term comprises a regularization term (e.g., aweight decay term in a neural network) such that the following selectionmodel is generated as follows:

${J\left( {X,Z} \right)}_{i} = {{\frac{1}{2}{\sum\limits_{i = 1}^{m}{{c^{i} - z^{i}}}^{2}}} + {\frac{S^{i}}{2} \cdot {W}^{2}}}$

X is comprises training data, Z comprises reconstructed data, and acomprises a hidden value of the auto encoders thereby generating themost fitted values trained over an explicit dataset such thatcorresponding features are used to build a J-score. Additionally (toachieve further refinement), the learning rules may be employed usingsparse auto encoder learning code to minimize the reconstruction errorwith a sparsity constraint (i.e., a sparse auto encoder). The aboveprocess is enabled via Kullback-Leibler divergence using backpropagationand limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) to trainthe model.

FIG. 7 illustrates execution of step 218 of FIG. 2 for generatingsoftware operational solution 700, in accordance with embodiments of thepresent invention. Execution of step 218 results in finalizing softwareapplications for software operational solution 700. Recommendations fora highest fitment software application for each feature are generated byapplying syndication over a trained data set to refine fitment scoressuch that:

1. Fitment scores are refined by applying syndication over trained data.2. An ordered set of depthfit is created to bring recommended softwareapplications to the top of a queue.3. Flexibility for decision making is provided such that any fine tuningof dimensions may provide updated recommendations for softwareapplications.

DepthFit fitment code is generated via usage of syndication over atrained data set which has been refined to ĵ as:

$P_{i} = {{\hat{J}\left( {X,Z} \right)}_{i}{\forall{\hat{J_{\iota}}\epsilon \left\{ {{J\left( {X,Z} \right)}_{i\;} + {\beta {\sum\limits_{j = 1}^{K}{{KL}\left( {\frac{1}{m}{\sum\limits_{i = 1}^{m}\left\lceil c^{i} \right\rceil}} \right)}}}} \right\}}}}$

The above equation represents an ordered set to select a best softwareproduct according to a depthFit assortment as follows:

{(P _(depth))}←{P _(i) ::∃P←(D _(H) ∪D _(M) ∪D _(L))_(i) |{D _(H) ,D_(L) ,D _(M) }∈{G}}

FIG. 8 illustrates a computer system 90 (e.g., server 23, databasesystem 21, and hardware device 14 of FIG. 1) used by or comprised by thesystem of FIG. 1 for improving hardware device operation by generatingevaluation and modification software code for validating softwareapplications and a hardware device, in accordance with embodiments ofthe present invention.

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module,” or “system.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing apparatus receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, device(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing device to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing device, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing device, and/or other devicesto function in a particular manner, such that the computer readablestorage medium having instructions stored therein comprises an articleof manufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing device, or other device tocause a series of operational steps to be performed on the computer,other programmable device or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable device, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The computer system 90 illustrated in FIG. 8 includes a processor 91, aninput device 92 coupled to the processor 91, an output device 93 coupledto the processor 91, and memory devices 94 and 95 each coupled to theprocessor 91. The input device 92 may be, inter alia, a keyboard, amouse, a camera, a touchscreen, etc. The output device 93 may be, interalia, a printer, a plotter, a computer screen, a magnetic tape, aremovable hard disk, a floppy disk, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes algorithms (e.g., the algorithm of FIG. 2 for improvinghardware device operation by generating evaluation and modificationsoftware code for validating software applications and a hardwaredevice. The processor 91 executes the computer code 97. The memorydevice 94 includes input data 96. The input data 96 includes inputrequired by the computer code 97. The output device 93 displays outputfrom the computer code 97. Either or both memory devices 94 and 95 (orone or more additional memory devices Such as read only memory device96) may include algorithms (e.g., the algorithm of FIG. 2) and may beused as a computer usable medium (or a computer readable medium or aprogram storage device) having a computer readable program code embodiedtherein and/or having other data stored therein, wherein the computerreadable program code includes the computer code 97. Generally, acomputer program product (or, alternatively, an article of manufacture)of the computer system 90 may include the computer usable medium (or theprogram storage device).

In some embodiments, rather than being stored and accessed from a harddrive, optical disc or other writeable, rewriteable, or removablehardware memory device 95, stored computer program code 84 (e.g.,including algorithms) may be stored on a static, nonremovable, read-onlystorage medium such as a Read-Only Memory (ROM) device 85, or may beaccessed by processor 91 directly from such a static, nonremovable,read-only medium 85. Similarly, in some embodiments, stored computerprogram code 97 may be stored as computer-readable firmware 85, or maybe accessed by processor 91 directly from such firmware 85, rather thanfrom a more dynamic or removable hardware data-storage device 95, suchas a hard drive or optical disc.

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service supplier who offers to improve hardware deviceoperation by generating evaluation and modification software code forvalidating software applications and a hardware device. Thus, thepresent invention discloses a process for deploying, creating,integrating, hosting, maintaining, and/or integrating computinginfrastructure, including integrating computer-readable code into thecomputer system 90, wherein the code in combination with the computersystem 90 is capable of performing a method for enabling a process forimproving hardware device operation by generating evaluation andmodification software code for validating software applications and ahardware device. In another embodiment, the invention provides abusiness method that performs the process steps of the invention on asubscription, advertising, and/or fee basis. That is, a servicesupplier, such as a Solution Integrator, could offer to enable a processfor improving hardware device operation by generating evaluation andmodification software code for validating software applications and ahardware device. In this case, the service supplier can create,maintain, support, etc. a computer infrastructure that performs theprocess steps of the invention for one or more customers. In return, theservice supplier can receive payment from the customer(s) under asubscription and/or fee agreement and/or the service supplier canreceive payment from the sale of advertising content to one or morethird parties.

While FIG. 8 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 8. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

What is claimed is:
 1. An automated information technology (IT) systemoperational improvement method comprising: identifying, by a processorof a hardware device of an IT system, a set of pre-tested softwareapplications associated with requirements of processes executed by saidhardware device with respect to said IT system, wherein saidrequirements are comprised by metadata, and wherein said set ofpre-tested software applications have been pre-tested for accuratefunctionality with respect to performing said processes executed by saidhardware device with respect to said IT system; generating, by saidprocessor based on results of said identifying, a list of currentlyavailable software applications of said set of pre-tested softwareapplications associated with required features associated with softwarebased solutions for modifying said set of pre-tested softwareapplications such that said currently available software applicationsare configured to provide and execute said required features;generating, by said processor based on said software based solutions,evaluation code for evaluating said currently available softwareapplications with respect to associations between each required featureof said required features and a plurality of hardware implemented datarepositories comprising information describing each required feature;generating, by said processor based on results of executing saidevaluation code, a list of validated software applications of saidcurrently available software applications, said validated softwareapplications associated with each said required feature being executedby applying Euclidean distance software code; generating, by saidprocessor based on results of said executing said evaluation code, alist of short listed software applications of said currently availablesoftware applications, said short listed software applications have beenverified via a sampling software test determined by feature translationlearning code associated with each said required feature being executed;generating, by said processor, identification software code enabling anautomated encoder learning process applying a feature learning techniquefor identifying said validated software applications; identifying, bysaid processor, a software operational solution of said software basedsolutions, said software operational solution comprising all availablefeatures of said required features associated with said validatedsoftware applications and comprising a greater number of features thanany other software application of said validated software applications;generating, by said processor based on said software operationalsolution, modification code for modifying said validated softwareapplications; and executing, by said processor, said modification coderesulting in improved operation of said validated software applicationsand said hardware device.
 2. The method of claim 1, further comprising:generating, by said processor, executable code associated with saidsoftware operational solution; executing, by said processor, saidexecutable code resulting in modifying said validated softwareapplications with respect to improving an efficiency and accuracy ofsaid validated software applications; generating, by said processor inresponse to said executing, additional executable code; and combining,by said processor, said additional executable with said validatedsoftware applications resulting in said improving said efficiency andaccuracy of said validated software applications.
 3. The method of claim1, wherein said generating said evaluation code comprises: evaluatingsoftware applications of said validated software applications that havebeen executed successfully at a previous point in time.
 4. The method ofclaim 1, further comprising: recording by said processor, associationsbetween each of said validated software applications.
 5. The method ofclaim 1, further comprising: generating, by said processor based onsoftware based solutions, sampling software code by applying a samplingtechnique for enabling said hardware device to execute self learningsoftware code with respect to a plurality of database systems.
 6. Themethod of claim 1, further comprising: associating, by said processor,said validated software applications to differing dimensions ofspecialized memory devices.
 7. The method of claim 1, wherein saidimproved operation of said validated software applications and saidhardware device comprises an improved processing speed for saidprocessor.
 8. The method of claim 1, wherein said improved operation ofsaid validated software applications and said hardware device comprisesan improved memory structure of said hardware device, and wherein saidimproved memory structure enables an improved access speed for accessingdata within said improved memory structure via an internal layeredstructure of said improved memory structure.
 9. The method of claim 1,further comprising: providing at least one support service for at leastone of creating, integrating, hosting, maintaining, and deployingcomputer-readable code in the hardware device, said code being executedby the processor to implement: said identifying said set of pre-testedsoftware applications, said generating said list of currently availablesoftware applications, said generating said evaluation code, saidgenerating said list of validated software applications, said generatingsaid list of short listed software applications, said generating aididentification software code, said identifying said software operationalsolution, said generating said modification code, and said executing.10. A computer program product, comprising a computer readable hardwarestorage device storing a computer readable program code, said computerreadable program code comprising an algorithm that when executed by aprocessor of a hardware device of an information technology (IT) systemimplements an automated IT system operational improvement method, saidmethod comprising: identifying, by said processor, a set of pre-testedsoftware applications associated with requirements of processes executedby said hardware device with respect to said IT system, wherein saidrequirements are comprised by metadata, and wherein said set ofpre-tested software applications have been pre-tested for accuratefunctionality with respect to performing said processes executed by saidhardware device with respect to said IT system; generating, by saidprocessor based on results of said identifying, a list of currentlyavailable software applications of said set of pre-tested softwareapplications associated with required features associated with softwarebased solutions for modifying said set of pre-tested softwareapplications such that said currently available software applicationsare configured to provide and execute said required features;generating, by said processor based on said software based solutions,evaluation code for evaluating said currently available softwareapplications with respect to associations between each required featureof said required features and a plurality of hardware implemented datarepositories comprising information describing each required feature;generating, by said processor based on results of executing saidevaluation code, a list of validated software applications of saidcurrently available software applications, said validated softwareapplications associated with each said required feature being executedby applying Euclidean distance software code; generating, by saidprocessor based on results of said executing said evaluation code, alist of short listed software applications of said currently availablesoftware applications, said short listed software applications have beenverified via a sampling software test determined by feature translationlearning code associated with each said required feature being executed;generating, by said processor, identification software code enabling anautomated encoder learning process applying a feature learning techniquefor identifying said validated software applications; identifying, bysaid processor, a software operational solution of said software basedsolutions, said software operational solution comprising all availablefeatures of said required features associated with said validatedsoftware applications and comprising a greater number of features thanany other software application of said validated software applications;generating, by said processor based on said software operationalsolution, modification code for modifying said validated softwareapplications; and executing, by said processor, said modification coderesulting in improved operation of said validated software applicationsand said hardware device.
 11. The computer program product of claim 10,wherein said method further comprises: generating, by said processor,executable code associated with said software operational solution;executing, by said processor, said executable code resulting inmodifying said validated software applications with respect to improvingan efficiency and accuracy of said validated software applications;generating, by said processor in response to said executing, additionalexecutable code; and combining, by said processor, said additionalexecutable with said validated software applications resulting in saidimproving said efficiency and accuracy of said validated softwareapplications.
 12. The computer program product of claim 10, wherein saidgenerating said evaluation code comprises: evaluating softwareapplications of said validated software applications that have beenexecuted successfully at a previous point in time.
 13. The computerprogram product of claim 10, wherein said method further comprises:recording by said processor, associations between each of said validatedsoftware applications.
 14. The computer program product of claim 10,wherein said method further comprises: generating, by said processorbased on software based solutions, sampling software code by applying asampling technique for enabling said hardware device to execute selflearning software code with respect to a plurality of database systems.15. The computer program product of claim 10, wherein said methodfurther comprises: associating, by said processor, said validatedsoftware applications to differing dimensions of specialized memorydevices.
 16. The computer program product of claim 10, wherein saidimproved operation of said validated software applications and saidhardware device comprises an improved processing speed for saidprocessor.
 17. The computer program product of claim 10, wherein saidimproved operation of said validated software applications and saidhardware device comprises an improved memory structure of said hardwaredevice, and wherein said improved memory structure enables an improvedaccess speed for accessing data within said improved memory structurevia an internal layered structure of said improved memory structure. 18.A hardware device of an information technology (IT) system comprising aprocessor coupled to a computer-readable memory unit in the IT system,said memory unit comprising instructions that when executed by theprocessor implements an automated IT system operational improvementmethod comprising: identifying, by said processor, a set of pre-testedsoftware applications associated with requirements of processes executedby said hardware device with respect to said IT system, wherein saidrequirements are comprised by metadata, and wherein said set ofpre-tested software applications have been pre-tested for accuratefunctionality with respect to performing said processes executed by saidhardware device with respect to said IT system; generating, by saidprocessor based on results of said identifying, a list of currentlyavailable software applications of said set of pre-tested softwareapplications associated with required features associated with softwarebased solutions for modifying said set of pre-tested softwareapplications such that said currently available software applicationsare configured to provide and execute said required features;generating, by said processor based on said software based solutions,evaluation code for evaluating said currently available softwareapplications with respect to associations between each required featureof said required features and a plurality of hardware implemented datarepositories comprising information describing each required feature;generating, by said processor based on results of executing saidevaluation code, a list of validated software applications of saidcurrently available software applications, said validated softwareapplications associated with each said required feature being executedby applying Euclidean distance software code; generating, by saidprocessor based on results of said executing said evaluation code, alist of short listed software applications of said currently availablesoftware applications, said short listed software applications have beenverified via a sampling software test determined by feature translationlearning code associated with each said required feature being executed;generating, by said processor, identification software code enabling anautomated encoder learning process applying a feature learning techniquefor identifying said validated software applications; identifying, bysaid processor, a software operational solution of said software basedsolutions, said software operational solution comprising all availablefeatures of said required features associated with said validatedsoftware applications and comprising a greater number of features thanany other software application of said validated software applications;generating, by said processor based on said software operationalsolution, modification code for modifying said validated softwareapplications; and executing, by said processor, said modification coderesulting in improved operation of said validated software applicationsand said hardware device.
 19. The hardware device of claim 18, whereinsaid method further comprises: generating, by said processor, executablecode associated with said software operational solution; executing, bysaid processor, said executable code resulting in modifying saidvalidated software applications with respect to improving an efficiencyand accuracy of said validated software applications; generating, bysaid processor in response to said executing, additional executablecode; and combining, by said processor, said additional executable withsaid validated software applications resulting in said improving saidefficiency and accuracy of said validated software applications.
 20. Thehardware device of claim 18, wherein said generating said evaluationcode comprises: evaluating software applications of said validatedsoftware applications that have been executed successfully at a previouspoint in time.